arXiv subjects

Soung Chang Liew

Publications and source records attributed to Soung Chang Liew.

At least 55 records · Page 3Linked to original sources

arXiv2022

Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access

In this work, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In the time-slotted RSL-MUD scheme, active users generate and transmit data packets with random sparsity. In the non-time-slotted SSL-MUD scheme, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of the active users. Simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.

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arXiv2021

Partially Observable Minimum-Age Scheduling: The Greedy Policy

This paper studies the minimum-age scheduling problem in a wireless sensor network where an access point (AP) monitors the state of an object via a set of sensors. The freshness of the sensed state, measured by the age-of-information (AoI), varies at different sensors and is not directly observable to the AP. The AP has to decide which sensor to query/sample in order to get the most updated state information of the object (i.e., the state information with the minimum AoI). In this paper, we formulate the minimum-age scheduling problem as a multi-armed bandit problem with partially observable arms and explore the greedy policy to minimize the expected AoI sampled over an infinite horizon. To analyze the performance of the greedy policy, we 1) put forth a relaxed greedy policy that decouples the sampling processes of the arms, 2) formulate the sampling process of each arm as a partially observable Markov decision process (POMDP), and 3) derive the average sampled AoI under the relaxed greedy policy as a sum of the average AoI sampled from individual arms. Numerical and simulation results validate that the relaxed greedy policy is an excellent approximation to the greedy policy in terms of the expected AoI sampled over an infinite horizon.

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arXiv2021

Federated Edge Learning with Misaligned Over-The-Air Computation

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning. OAC, however, hinges on accurate channel-gain precoding and strict synchronization among the edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for federated edge learning and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent, samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our sum-product ML estimator is linear in the packet length and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is non-severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.

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arXiv2021

ROFA: An OFDMA system for Ultra-Reliable Wireless Industrial Networking

This paper proposes and demonstrates a PHY-layer design of a real-time prototype that supports Ultra-Reliable Communication (URC) in wireless infrastructure networks. The design makes use of Orthogonal Frequency Division Multiple Access (OFDMA) as a means to achieve URC. Compared with Time-Division Multiple Access (TDMA), OFDMA concentrates the transmit power to a narrower bandwidth, resulting in higher effective SNR. Compared with Frequency-Division Multiple Access (FDMA), OFDMA has higher spectrum efficiency thanks to the smaller subcarrier spacing. Although OFDMA has been introduced in 802.11ax, the purpose was to add flexibility in spectrum usage. Our Reliable OFDMA design, referred to as ROFA, is a clean-slate design with a single goal of ultra-reliable packet delivery. ROFA solves a number of key challenges to ensure the ultra-reliability: (1) a downlink-coordinated time-synchronization mechanism to synchronize the uplink transmission of users, with at most $0.1us$ timing offset; (2) an "STF-free" packet reception synchronization method that makes use of the property of synchronous systems to avoid packet misdetection; and (3) an uplink precoding mechanism to reduce the CFOs between users and the AP to a negligible level. We implemented ROFA on the Universal Software Radio Peripheral (USRP) SDR platform with real-time signal processing. Extensive experimental results show that ROFA can achieve ultra-reliable packet delivery ($PER<10^5$) with $11.5dB$ less transmit power compared with OFDM-TDMA when they use $3$ and $52$ subcarriers respectively.

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arXiv2021

Outage-Limit-Approaching Protograph LDPC Codes for Slow-Fading Wireless Communications

Block-fading (BF) channel, also known as slow-fading channel, is a type of simple and practical channel model that can characterize the primary feature of a number of wireless-communication applications with low to moderate mobility. Although the BF channel has received significant research attention in the past twenty years, designing low-complexity outage-limit-approaching error-correction codes (ECCs) is still a challenging issue. For this reason, a novel family of protograph low-density parity-check (LDPC) codes, called root-protograph (RP) LDPC codes, has been conceived recently. The RP codes can not only realize linear-complexity encoding and high-speed decoding with the help of a quasi-cyclic (QC) structure, but also achieve near-outage-limit performance in a variety of BF scenarios. In this article, we briefly review the design guidelines of such protograph codes with the aim of inspiring further research activities in this area.

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arXiv2021

Flow Sampling: Network Monitoring in Large-Scale Software-Defined IoT Networks

Software-defined Internet-of-Things networking (SDIoT) greatly simplifies the network monitoring in large-scale IoT networks by per-flow sampling, wherein the controller keeps track of all the active flows in the network and samples the IoT devices on each flow path to collect real-time flow statistics. There is a tradeoff between the controller's sampling preference and the balancing of loads among devices. On the one hand, the controller may prefer to sample some of the IoT devices on the flow path because they yield more accurate flow statistics. On the other hand, it is desirable to sample the devices uniformly so that their energy consumptions and lifespan are balanced. This paper formulates the flow sampling problem in large-scale SDIoT networks by means of a Markov decision process and devises policies that strike a good balance between these two goals. Three classes of policies are investigated: the optimal policy, the state-independent policies, and the index policies (including the Whittle index and a second-order index policies). The second-order index policy is the most desired policy among all: 1) in terms of performance, it is on an equal footing with the Whittle index policy, and outperforms the state-independent policies by much; 2) in terms of complexity, it is much simpler than the optimal policy, and is comparable to state-independent policies and the Whittle index policy; 3) in terms of realizability, it requires no prior information on the network dynamics, hence is much easier to implement in practice.

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arXiv2021

Ethna: Analyzing the Underlying Peer-to-Peer Network of the Ethereum Blockchain

The peer-to-peer (P2P) network of blockchain used to transport its transactions and blocks has a high impact on the efficiency and security of the system. The P2P network topologies of popular blockchains such as Bitcoin and Ethereum, therefore, deserve our highest attention. The current Ethereum blockchain explorers (e.g., Etherscan) focus on the tracking of block and transaction records but omit the characterization of the underlying P2P network. This work presents the Ethereum Network Analyzer (Ethna), a tool that probes and analyzes the P2P network of the Ethereum blockchain. Unlike Bitcoin that adopts an unstructured P2P network, Ethereum relies on the Kademlia DHT to manage its P2P network. Therefore, the existing analytical methods for Bitcoin-like P2P networks are not applicable to Ethereum. Ethna implements a novel method that accurately measures the degrees of Ethereum nodes. Furthermore, it incorporates an algorithm that derives the latency metrics of message propagation in the Ethereum P2P network. We ran Ethna on the Ethereum Mainnet and conducted extensive experiments to analyze the topological features of its P2P network. Our analysis shows that the Ethereum P2P network possesses a certain effect of small-world networks, and the degrees of nodes follow a power-law distribution that characterizes scale-free networks.

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arXiv2021

When Blockchain Meets AI: Optimal Mining Strategy Achieved By Machine Learning

This work applies reinforcement learning (RL) from the AI machine learning field to derive an optimal Bitcoin-like blockchain mining strategy without knowing the details of the blockchain network model. Previously, the most profitable mining strategy was believed to be honest mining encoded in the default blockchain protocol. It was shown later that it is possible to gain more mining rewards by deviating from honest mining. In particular, the mining problem can be formulated as a Markov Decision Process (MDP) which can be solved to give the optimal mining strategy. However, solving the mining MDP requires knowing the values of various parameters that characterize the blockchain network model. In real blockchain networks, these parameter values are not easy to obtain and may change over time. This hinders the use of the MDP model-based solution. In this work, we employ RL to dynamically learn a mining strategy with performance approaching that of the optimal mining strategy by observing and interacting with the network. Since the mining MDP problem has a non-linear objective function (rather than linear functions of standard MDP problems), we design a new multi-dimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multi-dimensional RL mining algorithm can still achieve the optimal performance over time-varying blockchain networks.

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arXiv2021

Speeding up Block Propagation in Blockchain Network: Uncoded and Coded Designs

We design and validate new block propagation protocols for the peer-to-peer (P2P) network of the Bitcoin blockchain. Despite its strong protection for security and privacy, the current Bitcoin blockchain can only support a low number of transactions per second (TPS). In this work, we redesign the current Bitcoin's networking protocol to increase TPS without changing vital components in its consensus-building protocol. In particular, we improve the compact-block relaying protocol to enable the propagation of blocks containing a massive number of transactions without inducing extra propagation latencies. Our improvements consist of (i) replacing the existing store-and-forward compact-block relaying scheme with a cut-through compact-block relaying scheme; (ii) exploiting rateless erasure codes for P2P networks to increase block-propagation efficiency. Since our protocols only need to rework the current Bitcoin's networking protocol and does not modify the data structures and crypto-functional components, they can be seamlessly incorporated into the existing Bitcoin blockchain. To validate our designs, we perform analysis on our protocols and implement a Bitcoin network simulator on NS3 to run different block propagation protocols. The analysis and experimental results confirm that our new block propagation protocols could increase the TPS of the Bitcoin blockchain by 100x without compromising security and consensus-building.

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arXiv2020

Design and Implementation of Time-Sensitive Wireless IoT Networks on Software-Defined Radio

Time-sensitive wireless networks are an important enabling building block for many emerging industrial Internet of Things (IoT) applications. Quick prototyping and evaluation of time-sensitive wireless technologies are desirable for R&D efforts. Software-defined radio (SDR), by allowing wireless signal processing on a personal computer (PC), has been widely used for such quick prototyping efforts. Unfortunately, because of the \textit{uncontrollable delay} between the PC and the radio board, SDR is generally deemed not suitable for time-sensitive wireless applications that demand communication with low and deterministic latency. For a rigorous evaluation of its suitability for industrial IoT applications, this paper conducts a quantitative investigation of the synchronization accuracy and end-to-end latency achievable by an SDR wireless system. To this end, we designed and implemented a time-slotted wireless system on the Universal Software Radio Peripheral (USRP) SDR platform. We developed a time synchronization mechanism to maintain synchrony among nodes in the system. To reduce the delays and delay jitters between the USRP board and its PC, we devised a {\textit{Just-in-time}} algorithm to ensure that packets sent by the PC to the USRP can reach the USRP just before the time slots they are to be transmitted. Our experiments demonstrate that $90\%$ ($100\%$) of the time slots of different nodes can be synchronized and aligned to within $ \pm 0.5$ samples or $ \pm 0.05μs$ ($ \pm 1.5$ samples or $ \pm 0.15μs$), and that the end-to-end packet delivery latency can be down to $3.75ms$. This means that SDR-based solutions can be applied in a range of IIoT applications that require tight synchrony and moderately low latency, e.g., sensor data collection, automated guided vehicle (AGV) control, and Human-Machine-Interaction (HMI).

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arXiv2020

Sporadic Ultra-Time-Critical Crowd Messaging in V2X

Life-critical warning message, abbreviated as warning message, is a special event-driven message that carries emergency warning information in Vehicle-to-Everything (V2X). Three important characteristics that distinguish warning messages from ordinary vehicular messages are sporadicity, crowding, and ultra-time-criticality. In other words, warning messages come only once in a while in a sporadic manner; however, when they come, they tend to come as a crowd and they need to be delivered in short order. This paper puts forth a medium-access control (MAC) protocol for warning messages. To circumvent potential inefficiency arising from sporadicity, we propose an override network architecture whereby warning messages are delivered on the spectrum of the ordinary vehicular messages. Specifically, a vehicle with a warning message first sends an interrupt signal to pre-empt the transmission of ordinary messages, so that the warning message can use the wireless spectrum originally allocated to ordinary messages. In this way, no exclusive spectrum resources need to be pre-allocated to the sporadic warning messages. To meet the crowding and ultra-time-criticality aspects, we use advanced channel access techniques to ensure highly reliable delivery of warning messages within an ultra-short time in the order of 10 ms. In short, the overall MAC protocol operates by means of interrupt-and-access.

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arXiv2020

Is Multichannel Access Useful in Timely Information Update?

This paper investigates information freshness of multichannel access in information update systems. Age of information (AoI) is a fundamentally important metric to characterize information freshness, defined as the time elapsed since the generation of the last successfully received update. When multiple devices share the same wireless channel to send updates to a common receiver, an interesting question is whether dividing the whole channel into several subchannels will lead to better AoI performance. Given the same frequency band, dividing it into different numbers of subchannels lead to different transmission times and packet error rates (PER) of short update packets, thus affecting information freshness. We focus on a multichannel access system where different devices take turns to transmit with a cyclic schedule repeated over time. We first derive the average AoI by estimating the PERs of short packets. Then we examine bounded AoI, for which the instantaneous AoI is required to be below a threshold a large percentage of the time. Simulation results indicate that multichannel access can provide low average AoI and uniform bounded AoI simultaneously across different received powers. Overall, our investigations provide insights into practical designs of multichannel access systems with AoI requirements.

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arXiv2020

Game-Theoretical Analysis of Mining Strategy for Bitcoin-NG Blockchain Protocol

Bitcoin-NG, a scalable blockchain protocol, divides each block into a key block and many micro blocks to effectively improve the transaction processing capacity. Bitcoin-NG has a special incentive mechanism (i.e. splitting transaction fees to the current and the next leader) to maintain its security. However, this design of the incentive mechanism ignores the joint effect of transaction fees, mint coins and mining duration lengths on the expected mining reward. In this paper, we identify the advanced mining attack that deliberately ignores micro blocks to enlarge the mining duration length to increase the likelihood of winning the mining race. We first show that an advanced mining attacker can maximize its expected reward by optimizing its mining duration length. We then formulate a game-theoretical model in which multiple mining players perform advanced mining to compete with each other. We analyze the Nash equilibrium for the mining game. Our analytical and simulation results indicate that all mining players in the mining game converge to having advanced mining at the equilibrium and have no incentives for deviating from the equilibrium; the transaction processing capability of the Bitcoin-NG network at the equilibrium is decreased by advanced mining. Therefore, we conclude that the Bitcoin-NG blockchain protocol is vulnerable to advanced mining attack. We discuss how to reduce the negative impact of advanced mining for Bitcoin-NG.

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arXiv2020

New Transceiver Designs for Interleaved Frequency Division Multiple Access

This paper puts forth a class of new transceiver designs for interleaved frequency division multiple access (IFDMA) systems. These transceivers are significantly less complex than conventional IFDMA transceiver. The simple new designs are founded on a key observation that multiplexing and demultiplexing of IFDMA data streams of different sizes are coincident with the IFFTs and FFTs of different sizes embedded within the Cooley-Tukey recursive FFT decomposition scheme. For flexible resource allocation, this paper puts forth a new IFDMA resource allocation framework called Multi-IFDMA, in which a user can be allocated multiple IFDMA streams. Our new transceivers are unified designs in that they can be used in conventional IFDMA as well as multi-IFDMA systems. Two other well-known multiple-access schemes are localized FDMA (LFDMA) and orthogonal FDMA (OFDMA). In terms of flexibility in resource allocation, Multi-IFDMA, LFDMA, and OFDMA are on an equal footing. With our new transceiver designs, however, IFDMA has the following advantages (besides other known advantages not due to our new transceiver designs): 1) IFDMA/Multi-IFDMA transceivers are significantly less complex than LFDMA transceivers; in addition, IFDMA/Multi-IFDMA has better Peak-to-Average Power Ratio (PAPR) than LFDMA; 2) IFDMA/Multi-IFDMA transceivers and OFDMA transceivers are comparable in complexity; but IFDMA/Multi-IFDMA has significantly better PAPR than OFDMA.

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arXiv2020

PubChain: A Decentralized Open-Access Publication Platform with Participants Incentivized by Blockchain Technology

We design and implement Publication Chain (PubChain), a decentralized open-access publication platform built on decentralized and distributed technologies of blockchain and IPFS peer-to-peer file sharing systems. The existing publication platforms have some severe drawbacks. First, instead of promoting widespread knowledge sharing, access to publications on the platforms owned by publishers is often on a fee basis. This drawback of pay wall prevents researchers from "standing on the shoulders of giants". Moreover, the peer review process on most all existing publication platforms (including both open-access and publisher platforms) is prone to be ineffective, since there is no proper incentive to reviewers for performing high-qualified reviews. PubChain is an alternative platform to the existing publication venues aiming to address their drawbacks. No central third-party owns the contents (i.e., papers and reviews) of PubChain. Exploiting blockchain technology, we devise an elaborate incentive scheme on PubChain to incentivize key stakeholders (i.e., authors, readers and reviewers) to participate publication activities on PubChain in a substantive manner by earning credits and rewards through self-motivated interactions. We have performed simulations to investigate the robustness of our proposed incentive scheme against fraudulent publications and reviews. We also have implemented a prototype of PubChain to demonstrate its key concepts.

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arXiv2020

Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels

This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global $α$-fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly received, so that the agent can optimize its strategy based on the received reward. In our wireless application where the channels are noisy, the feedback/reward (i.e., the ACK packet) may be lost due to channel noise and interference. Without correct feedback, the agent (i.e., the network user) may fail to find a good solution. Moreover, in the distributed protocol, each agent makes decisions on its own. It is a challenge to guarantee that the multiple agents will make coherent decisions and work together to achieve the same objective, particularly in the face of imperfect feedback channels. To tackle the challenge, we put forth (i) a feedback recovery mechanism to recover missing feedback information, and (ii) a two-stage action selection mechanism to aid coherent decision making to reduce transmission collisions among the agents. Extensive simulation results demonstrate the effectiveness of these two mechanisms. Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted.

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arXiv2020

Flexible Subcarrier Allocation for Interleaved Frequency Division Multiple Access

Interleaved Frequency Division Multiple Access (IFDMA) and Orthogonal FDMA (OFDMA) belong to a class of signal modulation and multiple-access techniques in which information of multiple users are multiplexed and carried on subcarriers within a shared spectrum. Compared with OFDMA, IFDMA has lower Peak-to-Average Power Ratio (PAPR). However, IFDMA poses two rigid constraints on subcarrier allocation: 1) the subcarriers occupied by a user must be evenly-spaced among the available subcarriers. 2) the number of subcarriers used by a user must be a divisor of the total number of subcarriers. This paper investigates how to overcome these constraints to allow flexible and fine-grained subcarrier allocation in IFDMA. Specifically, we put forth 1) a bit-reversal subcarrier allocation scheme whereby the problem of allocating evenly-spaced subcarriers is transformed to a more intuitive problem of filling contiguous bins; 2) a multi-stream IFDMA scheme whereby a user can have an arbitrary number of subcarriers. For the synchronous scenario in which user requests arrive in a synchronous manner, we show that IFDMA can achieve the same level of flexibility and granularity as OFDMA in subcarrier allocation. For the asynchronous scenario in which user requests arrive and depart asynchronously, we show that the blocking probability of IFDMA is only slightly worse than that of OFDMA: specifically, the gap between the blocking probabilities of IFDMA and OFDMA is only 2:56% at a moderate offered load.

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arXiv2019

Information Update: TDMA or FDMA?

This paper studies information freshness in information update systems operated with TDMA and FDMA. Information freshness is characterized by a recently introduced metric, age of information (AoI), defined as the time elapsed since the generation of the last successfully received update. In an update system with multiple users sharing the same wireless channel to send updates to a common receiver, how to divide the channel among users affects information freshness. We investigate the AoI performances of two fundamental multiple access schemes, TDMA and FDMA. We first derive the time-averaged AoI by estimating the packet error rate of short update packets based on Gallager's random coding bound. For time-critical systems, we further define a new AoI metric, termed bounded AoI, which corresponds to an AoI threshold for the instantaneous AoI. Specifically, the instantaneous AoI is below the bounded AoI a large percentage of the time. We give a theoretical upper bound for bounded AoI. Our simulation results are consistent with our theoretical analysis. Although TDMA outperforms FDMA in terms of average AoI, FDMA is more robust against varying channel conditions since it gives a more stable bounded AoI across different received powers. Overall, our findings give insight to the design of practical multiple access systems with AoI requirements.

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